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Virginia Tech

1. Yeoh, SengMing. Secure and Efficient In-Process Monitor and Multi-Variant Execution.

Degree: MS, Computer Engineering, 2021, Virginia Tech

URL: http://hdl.handle.net/10919/102158 

Memory corruption attacks are still prevalent on modern software. While there have been many attempts at hardening software and preventing against these attacks, the heavy performance cost of running these defenses and intrusive modifications required have proven to be a barrier to adoption. In this work, we present L-MVX, a high-performance hardware assisted in-process monitor protection system that provides an unintrusive and efficient way to defend against these attacks on monitor systems. We also introduce L-MVX, a flexible lightweight process monitoring engine running on L-MVX that aims to solve some of the performance problems of recent monitor defenses. Advisors/Committee Members: Ravindran, Binoy (committeechair), Wang, Haining (committee member), Nikolaev, Ruslan (committee member).

Subjects/Keywords: Multi-Variant Execution; In-Process Monitor; Memory Isolation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yeoh, S. (2021). Secure and Efficient In-Process Monitor and Multi-Variant Execution. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/102158

Chicago Manual of Style (16th Edition):

Yeoh, SengMing. “Secure and Efficient In-Process Monitor and Multi-Variant Execution.” 2021. Masters Thesis, Virginia Tech. Accessed March 01, 2021. http://hdl.handle.net/10919/102158.

MLA Handbook (7th Edition):

Yeoh, SengMing. “Secure and Efficient In-Process Monitor and Multi-Variant Execution.” 2021. Web. 01 Mar 2021.

Vancouver:

Yeoh S. Secure and Efficient In-Process Monitor and Multi-Variant Execution. [Internet] [Masters thesis]. Virginia Tech; 2021. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/10919/102158.

Council of Science Editors:

Yeoh S. Secure and Efficient In-Process Monitor and Multi-Variant Execution. [Masters Thesis]. Virginia Tech; 2021. Available from: http://hdl.handle.net/10919/102158

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